Autoencoders [1]} are another Neural Network used
to reproduce the inputs in a compressed fashion. Autoencoder has a
special property in which the number of input neurons is the same as the
number of output neurons. See the Figure REF . The goal of
Autoencoder is to create a representation of the input at the output
layer such that both output and input are similar but the actual use of
the Autoencoder is for determining a compressed version of the input
data with the lowest amount of loss in data. This is very similar to
what Principal Component Analysis does, in a black-box manner. Encoder
part of Autoencoder compresses the data at the same time ensuring that
the important data is not lost but the size of the data is reduced.
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